A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes

Aim To identify, predict and validate distinct glycemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient-centred care. Material and methods We conducted a retrospective study on two cohorts using routinely collected individual patient data in primary care practices from two large Dutch diabetes patient registries. Participants included newly diagnosed, adult patients with type 2 diabetes between January 2006 and December 2014 (n = 10,528, development cohort; n = 3,777, validation cohort). Latent growth mixture modeling (LGMM) identified distinct glycemic 5-year trajectories. Machine learning models were built to predict the trajectories with easily obtainable patient characteristics in daily clinical practice. Results Three different glycemic trajectories were identified: 1) stable, adequate glycemic control (76.5% of patients); 2) improved glycemic control (21.3% of patients) and 3) deteriorated glycemic control (2.2% of patients). Similar trajectories could be discerned in the validation cohort. BMI, HbA1c and triglycerides were the most important predictors of trajectory membership. The predictive model, trained on the development cohort, had a receiver operating characteristic area under the curve (ROC-AUC) of 0.96 in the validation cohort, indicating excellent accuracy. Conclusions The developed model can effectively explain heterogeneity in future glycemic response of patients with type 2 diabetes. It can therefore be used in clinical practice as a quick and easy tool to provide tailored diabetes care.